The course is dedicated to statistical and computational methods for the design and analysis of bioinformatics experiments.
The topics that will be covered in this course will likely include:
- Introduction to R and RStudio
- Introduction to cell biology
- Introduction to measurement technologies: microarrays, sequencing, SNPs and ChIP
- Basic statistics
- Gene Expression Microarrays: experimental designs, preprocessing and normalization, differential expression.
- RNA-seq: experimental designs, preprocessing and normalization, differential expression, splice variants
- Replication and pooling
- Gene Set enrichment analysis
- Clustering samples and genes
- Classifying samples using statistical machine learning
- Dimension reduction
- Combining data from multiple platforms
- Selected topics such as gene networks, time course experiments and project presentations as time permits
Here is a link to the Online Notes for STAT 555.
Dr. Naomi Altman is the primary author of these course materials.
This course makes extensive use if the R statistical software. See the Department of Statistics' Statistical Software page for information about obtaining a copy of R.
There will be no required text-book. Online course materials will combine methodological background description and presentation of analyses and results from recent articles. References and notes will be posted.
- 4 - 6 Homework Assignment (50% of grade)
- Individual Project and Presentation (50% of grade)
The course has no pre-requisites, but some computational skills and/or familiarity with basic concepts in statistics, bioinformatics and/or cell biology will help. Undergraduates must obtain consent of the instructors to register for the course.